http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Oded Koren (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
-
Big Data Performance Evaluation Analysis Using Apache Pig
Gal Engelberg,Oded Koren,Nir Perel 보안공학연구지원센터 2016 International Journal of Software Engineering and Vol.10 No.11
While companies' usage of big data products increases, the question of which big data architecture is the most suitable to the company's needs is rising. This study presents an approach of running multiple processes which simulates preliminary data processing of sale transactions input dataset using Apache Pig, in order to find the best performing big data environment in terms of decentralization level over the HDFS. The case study approach can provide companies an additional tool for understanding the required investment on hardware or cloud computing resources. We analyze which decentralization level achieves the best processing time, and explore the behavior of performance's change according to the change in decentralization level and performance change according to the change in the size of the input dataset. The case study's insights are: When processing the same data flow over the same input dataset, processing time performance is better as long as decentralization level increases; As long as decentralization level increases the change between performances decreases significantly; Processing the same Pig data flow under the same scale of decentralization level over large input dataset performs better then processing it over a smaller input dataset - in terms of processing time per volume unit; As blocks-data nodes ratio becomes higher, the processing time becomes longer, and vice versa.
-
Pig Vs. Hive Use Case Analysis
Danielle Kendal,Oded Koren,Nir Perel 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.12
Corporations are changing their practices to data-driven big data initiatives, as big data analytics has provided companies with the ability to grow their businesses and increase competition. As the importance of data analytics grew, so accordingly did the size of the data to analyze, thus demanding a more powerful data platform. This paper shows a case study of two High Level Query Languages that are constructed on top of Hadoop MapReduce; Pig and Hive. By creating a query in each query language, both resulting in an identical output, and by running each query 30 times on 2 different sized files (120 runs total), this comparison provides a statistically significant conclusion.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
